RIS-aided Single-frequency 3D Imaging by Exploiting Multi-view Image Correlations

TL;DR

The paper tackles single-frequency RIS-aided 3D imaging under bandwidth and pilot constraints by exploiting near-field multi-view correlations. It builds an anisotropy-aware multi-view model that accounts for occlusion and direction-dependent scattering via Markov and Gauss-Markov processes, and solves the joint multi-view inverse problem with the EM-turbo-GAMP algorithm. Theoretical analysis relates PSF to subpath correlations and establishes geometric constraints, while RIS phase-shift optimization further enhances resolvability. Numerical results show substantial improvements in imaging range, accuracy, and anisotropy characterization, with up to an 80% reduction in pilot overhead when combining multiple adjacent views, illustrating the practical viability of RIS-aided, low-overhead 3D imaging at a single frequency.

Abstract

Retrieving range information in three-dimensional (3D) radio imaging is particularly challenging due to the limited communication bandwidth and pilot resources. To address this issue, we consider a reconfigurable intelligent surface (RIS)-aided uplink communication scenario, generating multiple measurements through RIS phase adjustment. This study successfully realizes 3D single-frequency imaging by exploiting the near-field multi-view image correlations deduced from user mobility. We first highlight the significance of considering anisotropy in multi-view image formation by investigating radar cross-section properties and diffraction resolution limits. We then propose a novel model for joint multi-view 3D imaging that incorporates occlusion effects and anisotropic scattering. These factors lead to slow image support variation and smooth coefficient evolution, which are mathematically modeled as Markov processes. Based on this model, we employ the Expectation Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint multi-view single-frequency 3D imaging with limited measurements. Simulation results reveal the superiority of joint multi-view imaging in terms of enhanced imaging ranges, accuracies, and anisotropy characterization compared to single-view imaging. Combining adjacent observations for joint multi-view imaging enables a reduction in the measurement overhead by 80%.

Figures (13)

• Figure 1: Illustration of the considered RIS-aided communication system.
• Figure 2: (a) Considered rectangular PEC scatterer; (b) Comparison of RCS values with different pixel sizes ($\lambda / 4$ and $\lambda$) under isotropic assumptions; (c) Normalized RCS values of single pixels versus scattering angles (the legends represent pixel sizes, except that "tol 1 dB" denotes the tolerance line of $1\ \rm{dB}$).
• Figure 3: Illustration of the proposed multi-view image model considering occlusion effects and anisotropic scattering, where white squares represent zero coefficients, and colorful squares denote varying non-zero coefficients.
• Figure 4: Illustration of the EM-turbo-GAMP algorithm for joint multi-view imaging, where the UE moves along a continuous trajectory (circles and squares in the right part of this figure represent random variables and probability distribution factors, respectively).
• Figure 5: (a) The imaging scenario, where the blue, green, and brown voxels denote the considered voxel, and those in the range and cross-range directions, respectively; (b) Subpath correlation with $n_1 = 500$, where the center spike, sidelobes, and sub-sidelobes are marked with a triangle, squares, and circles, respectively; (c) Maximum sidelobes with respect to the distance $D$ with different RIS sizes and voxel sizes.

Theorems & Definitions (6)

• Remark 1
• Remark 2
• Remark 3
• Remark 4
• Theorem 1
• Remark 5